class_docstr: CIFAR10 data set from https://www.cs.toronto.edu/~kriz/cifar.html
  
  Arguments:
      path (str): Local path to copy data files.
      normalize (bool): Flag to normalize data.
      whiten (bool): Flag to apply whitening transform.
      pad_classes (bool): Flag to pad out class count to 16
                          for compatibility with conv layers on GPU.
code: def gen_iterators(self):
      datasets = self.load_data()
  
      (X_train, y_train), (X_test, y_test), nclass = datasets
      if self.pad_classes:
          nclass = 16
  
      train = ArrayIterator(
          X_train,
          y_train,
          nclass=nclass,
          lshape=(3, 32, 32),
          name="train",
      )
      test = ArrayIterator(
          X_test,
          y_test,
          nclass=nclass,
          lshape=(3, 32, 32),
          name="valid",
      )
      self._data_dict = {"train": train, "valid": test}
      return self._data_dict
  
imports: from __future__ import division
  from future import standard_library
  from builtins import str
  import gzip
  import logging
  import numpy
  import os
  import tarfile
  from neon.util.compat import pickle
  from neon.util.compat import pickle_load
  from neon.data.datasets import Dataset
  from neon.data.dataiterator import ArrayIterator
label_desc: Use image node_id 3 for calling the CIFAR10 obj's underlying member method code with example usage: obj.gen_iterators() and returns: self